We present a fully memristive spiking neural network (MSNN) consisting of
novel memristive neurons trained using the backpropagation through time (BPTT)
learning rule. Gradient descent is applied directly to the memristive
integrated-and-fire (MIF) neuron designed using analog SPICE circuit models,
which generates distinct depolarization, hyperpolarization, and repolarization
voltage waveforms. Synaptic weights are trained by BPTT using the membrane
potential of the MIF neuron model and can be processed on memristive crossbars.
The natural spiking dynamics of the MIF neuron model are fully differentiable,
eliminating the need for gradient approximations that are prevalent in the
spiking neural network literature. Despite the added complexity of training
directly on SPICE circuit models, we achieve 97.58% accuracy on the MNIST
testing dataset and 75.26% on the Fashion-MNIST testing dataset, the highest
accuracies among all fully MSNNs.

本文提出了一种完全基于 memristive spiking 神经网络的 MSNN，采用 backpropagation through time (BPTT) 学习规则训练了新型 memristive 神经元，并利用 analog SPICE 电路模型直接应用梯度下降的方式来对神经元进行训练，同时实现可处理在 memristive crossbars 上的突触权重训练。